Control of multifunctional prosthetic hands by processing the electromyographic signal.

By M. Zecca, Silvestro Micera, Maria Chiara Carrozza, Paolo Dario. Critical reviews in biomedical engineering, 2002

621 citations recorded by the source corpus.

The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contray, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none ofthese methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/fee3a8542b873b34bada4883ecef1b37f4eb9a26

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

EMG feature evaluation for improving myoelectric pattern recognition robustness (2013, background): “Most of related literature on EMG pattern recognition focuses on the improvement of classification accuracy and the number of discriminated motions (Oskoei & Hu, 2007; Zecca et al., 2002).”
A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems (2005, background): “, 2002), and also for recording EMG signals devoted to artificial limb control (Zardoshti-Kermani et al., 1995; Zecca et al., 2002) or for non-invasive slow brain– computer interfaces (Pfurtscheller et al.”
Feature reduction and selection for EMG signal classification (2012, methodology): “Although many research works have mainly tried to explore and examine an appropriate feature vector for numerous specific EMG signal classification applications (e.g. Boostani & Moradi, 2003; Oskoei & Hu, 2008; Phinyomark et al., 2009a; Zardoshti-Kermani et al., 1995; Zecca et al., 2002), there have”
Brain-machine interfaces: past, present and future. (2006, background): “39 Zecca, M. et al. (2002) Control of multifunctional prosthetic hands by”
Myoelectric forearm prostheses: state of the art from a user-centered perspective. (2011, background): “Hargrove LJ, Losier Y, Lock B, Englehart K, Hudgins B.”
Decoding of Individuated Finger Movements Using Surface Electromyography (2009, background): “Its value correlates to muscle contraction levels [ 8 ].”
On the Shared Control of an EMG-Controlled Prosthetic Hand: Analysis of User–Prosthesis Interaction (2008, methodology): “commercial hand prosthesis [3]: because of the excessive effort required to reliably use more than two EMG signals extracted from the residual upper extremity muscle contractions [4]–[6], current prosthetic hands (such as OttoBock hands [1]) have only one or two DOFs [1]; therefore, they are not dex”
Hand Posture Subspaces for Dexterous Robotic Grasping (2009, neutral): “A number of possible approaches are described in the literature, including electromyography (Zecca et al. 2002) and cortical implants (Taylor et al.”
Design of a cybernetic hand for perception and action (2006, methodology): “Using such signals is a simple and effective approach to obtaining commands for controlling active prosthetic hands (Nader 1990; for a review see Zecca et al. (2002)), and it is used, for instance, in Otto Bock hands.”